Rule-bound

In his charming and entertaining way, Dr. Jeremy Fox very nonchalantly ends his ‘Advice : tips on stats’(in the ‘Advice’ series of posts in Dynamic Ecology) – “Don’t just blindly follow the “rules”. Rules are not a substitute for thought.” Having given up Math as a subject after 12th standard, confronting statistics suddenly and out of the blue at the PhD level was quite a rude shock for me. To make matters worse, most of my classmates seemed to know the fundamentals of stats, why one did statistical tests and what statistical software to use. I unfortunately also attended a statistics course (ah, those credits are a bane) in a non-Biological Sciences department and (no prizes for guessing) got thoroughly overwhelmed. But the worst was yet to come – my own data were to give me the most vivid nightmares of them all (I have been grappling with very naughty data sets for a very long time without being able to convince reviewers that they are in fact very well behaved).

As a naive user of statisconfusedtics, I wonder if it is as simple to ‘not blindly follow rules’. I intend to use statistics merely as a tool to state with conviction that what I observe in nature is reasonably true (hopefully at p < 0.05, or 0.1 whenever ‘ecologically relevant’; but let’s put that discussion off for another time). And unfortunately, the only way I can ensure that I have done the statistics bit correctly, is if I have followed the rules. For the inexperienced, a set of rules to follow is often the only way forward in the confusing maze of multiple ways of achieving the same thing.

Dr. Fox uses subtle metaphors (in a way only experienced scientists can) to make the task of doing good stats sound less daunting – “Statistics is like cooking—there are recipes to follow, but not all of them are good ones, and even the good ones are treated more like guidelines by the best cooks, who can judge when, why, and how to deviate from the recipe”. How does one become a ‘good cook’? I guess the best cooks start off following the rules before they put creativity and thought into the process. When a subject is alien and unintuitive, is it really that simple to start with a ‘thought’ than with a tried and tested ‘rule’? I was wondering if everyone feels the same way or if I am the only one here who completely missed the boat? (I exclude the physicist and the mathematician amongst us from this discussion; their practical advice would be to stop doing statistics altogether. I second that motion.)

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5 comments

  1. harisridhar

    I third the motion to do away with stats altogether:) Suhel Quader, at YETI Dehradun, suggested one way to do this: by asking trivial questions, e.g. is the grass greener where the cows haven’t grazed?:)
    More seriously, a way to reduce the pain of statistics might lie in trying to come up with more elegant/simple sampling designs i.e. simplify design in such a way that only the most straightforward statistics are required. Of course, this is easier said than done, for most of ecology. Another issue seems to be, as Brian McGill points in this very insightful post (http://dynamicecology.wordpress.com/2012/09/11/statistical-machismo/), a tendency to use more complicated (macho?) statistics where simple ones will do.
    A couple of nice papers in this context:
    1. Stewart-Oaten, Allan. 1995. Rules and Judgments in Statistics: Three Examples. Ecology 76:2001–2009
    2. Murtaugh, Paul A. 2007. SIMPLICITY AND COMPLEXITY IN ECOLOGICAL DATA ANALYSIS. Ecology 88:56–62.

    • geetharamaswami

      I actually meant stats in general, not necessarily ‘macho’ stats. Simple stats has as stringent rules as well – exactly why studies need to be designed to match these rules :). (think about basic assumptions that you have to keep in mind every time you do that t-test). I was also wondering if the ‘rule’ v/s ‘thought’ dialogue can be extended to other aspects of research (such as experimental design itself). Why limit ourselves just to the analysis?

  2. Jeremy Fox

    Glad you liked the post.

    Yes, the way you learn good judgment is to first learn the rules, and (importantly) the reasons for those rules. Then, as you struggle to follow all the rules all the time when doing science, and as you talk about your struggles with colleagues who are more knowledgeable and experienced, you’ll start to learn good judgement about when and how to break the rules. Much as how we teach children black-and-white rules of good behavior (“Don’t lie”, “Don’t hit people”, etc.) and the reasons for them (“How would you like it if someone hit you?”). Then as our children grow up and try to live by those rules, they discover that there are few absolutes and most things are shades of grey. After all, there are contexts in which lying, or hitting someone, is actually the right thing to do.

    • geetharamaswami

      Thank you so much for responding to this post! I think the operative phrase here is ‘learning the reasons for the rules’. It is sometimes difficult to understand these reasons, especially when they are mathematically argued. Many students may prefer taking the short-cut to just following the rules rather than exploring the reasoning behind the rules.

  3. bhartidk

    As I am wrapping up analysis for the green turtle data, I can’t help the feeling that I’m cheating. : I
    Because even though my data is “naughty”, the cookbook that I follow offers help and tells me exactly what I should do to set things straight. And it asks me to skip the sections which explain the mathematics behind the analysis, if I am not interested.

    I have an intuitive understanding of what the analysis is doing, but I wonder if “it is right” to totally gloss over the math? Also makes me feel, I am myself not doing much thinking in the whole process.

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